fix(ci): ruff/mypy issues — lower-case doctest vars, assert weights before enumerate

Author: Mahadev-05

machine_learning/ridge_regression.py

@@ -13,10 +13,10 @@
 Examples
 --------
 >>> import numpy as np
->>> X = np.array([[1.0], [2.0], [3.0]])
+>>> x = np.array([[1.0], [2.0], [3.0]])
 >>> y = np.array([2.0, 4.0, 6.0])
 >>> model = RidgeRegression(learning_rate=0.1, lambda_=0.0, epochs=2000)
->>> model.fit(X, y)
+>>> model.fit(x, y)
 >>> np.allclose(model.weights, [0.0, 2.0], atol=1e-2)
 True
 >>> model.predict(np.array([[4.0], [5.0]]))
@@ -27,7 +27,6 @@
 
 from dataclasses import dataclass
 
-
 import httpx
 import numpy as np
 
@@ -165,6 +164,7 @@ def main() -> None:
     mae = mean_absolute_error(preds, y)
 
     print("Learned weights:")
+    assert model.weights is not None
     for i, w in enumerate(model.weights):
         print(f"w[{i}] = {w:.6f}")
     print(f"MAE on training data: {mae:.6f}")

machine_learning/tests/test_ridge_regression.py

@@ -55,11 +55,11 @@ def test_accepts_numpy_matrix():
     from machine_learning.ridge_regression import collect_dataset
 
     data = collect_dataset()
-    X = np.c_[data[:, 0].astype(float)]  # numpy.matrix
+    x = np.c_[data[:, 0].astype(float)]  # numpy.matrix
     y = np.ravel(data[:, 1].astype(float))
 
     model = RidgeRegression(learning_rate=0.0002, lambda_=0.01, epochs=500)
-    model.fit(X, y)
-    preds = model.predict(X)
+    model.fit(x, y)
+    preds = model.predict(x)
     assert preds.shape == (y.shape[0],)
     assert mean_absolute_error(preds, y) >= 0.0